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The purpose of this paper is to present issues and challenges of scheduled maintenance task development within the maintenance review board (MRB) process, and to find potential areas of improvement in the application of the MSG‐3 methodology for aircraft systems.

The issues and challenges as well as potential areas of improvement have been identified through a constructive review that consists of two parts. The first part is a benchmarking between the Maintenance Steering Group (MSG‐3) methodology and other established and documented versions of reliability‐centred maintenance (RCM). This benchmarking focuses on the MSG‐3 methodology and compares it with some RCM standards to identify differences and thereby find ways to facilitate the application of MSG‐3. The second part includes a discussion about methodologies and tools that can support different steps of the MSG‐3 methodology within the framework of the MRB process.

The MSG‐3 methodology is closely related to the RCM methodology, in which the anticipated consequences of failure are considered for risk evaluation. However, MSG‐3 considers neither environmental effects of failures nor operational consequences of hidden failures. Furthermore, in MSG‐3, the operational check (failure‐finding inspection) is given priority before all other tasks, whereas in RCM it is considered as a default action, where there is no other applicable and effective option. While RCM allows cost‐effectiveness analysis for all failures that have no safety consequences, MSG‐3 just allows it for failures with economic consequences. A maintenance program that is established through the MRB process fulfils the requirements of continuous airworthiness, but there is no foundation to claim that it is the optimal or the most effective program from an operator's point‐of‐view. The major challenge when striving to achieve a more effective maintenance program within the MRB process is to acquire supporting methodologies and tools for adequate risk analysis, for optimal interval assignments, and for selection of the most effective maintenance task.

The paper presents a critical review of existing aircraft scheduled maintenance program development methodologies, and demonstrates the differences between MSG‐3 and other RCM methodologies.

The purpose of this paper is to explore the learning effects of entrepreneurial failure (EF) among micro-women entrepreneurship in Accra.

Using a qualitative research method, purposive sampling was used to identify 64 micro-women entrepreneurs, and data was collected using interviews.

Linked to the experiential learning theory, the study found that consequences of EF provide an entrepreneurial learning platform that influences micro-women entrepreneurs to acquire new skills and knowledge for their entrepreneurial development, incurred costs that negatively affect operations, acquired no knowledge or take a decision to opt-out of entrepreneurship as a carrier.

The study was based on a relatively small sample size of 64 participants which made it difficult to generalize the findings despite the benefits of the research methods adopted in the study.

The study contributed to the EF literature with micro-women entrepreneurs in an African context. It highlights the possible additional learning consequences of EF which are being scared of entrepreneurial venturing as a result of their failure. This negatively impacts the desire to engage in entrepreneurship as a future career. The identification of irrelevant learning consequences of EF, suggests that the existing experiential learning theory may need to be revised to further enhance its applicability in micro-women entrepreneurship in different cultural contexts, as not all experiences result in learning.

The purpose of this research is to determine whether the type of tax services provided by a public accounting firm to its audit client and the consequence severity of an audit failure impact jurors' assessment of audit quality and auditor liability.

The authors administer a court case to 168 jurors manipulating three levels of tax services provided to an audit client (none, tax preparation, and aggressive tax planning services); two levels of consequence severity of the alleged audit failure, observing the impact on jurors' assessment of audit quality, auditor responsibility for audit failure; and damages awarded the plaintiff.

Consistent with recent US regulations, jurors perceive the quality of the audit to be lower when auditors provide aggressive tax planning services, but not for tax preparation services. Damages are greater when auditors provide aggressive tax planning services across both levels of consequence severity.

The results indicate that the type of tax services provided may impact jurors' views of audit quality and damage assessments against auditors. The questionnaire uses previously validated measures, but the results may not be generalizable to jurors in all jurisdictions.

Though empirical evidence is mixed at best about the impact of auditors providing non-audit services on auditor independence in fact, auditor independence in appearance, and thus audit quality, such impacts may affect the way jurors perceive the situation.

The study directly tests the implications for auditor liability of new restrictions on tax services and more accurately measures the impact of consequence severity, using actual jurors.

The paper aims to construct a method to simulate the relationship between the parameters of soil properties and the area of sliding mass of the true slip surface of a landslide.

The smoothed particle hydrodynamics (SPH) algorithm is used to calibrate a response surface function which is adopted to quantify the area of sliding mass of the true slip surface for each failure sample in Monte Carlo simulation. The proposed method is illustrated through a homogeneous and a heterogeneous cohesive soil slope.

The comparison of the results between the proposed method and the traditional method using the slip surface with minimum factor of safety (FS_min) to quantify the failure consequence has shown that the landslide risk tends to be attributed to a variety of risk sources, and that the use of a slip surface with FS_min to quantify the consequence of a landslide underestimates the landslide risk value. The difference of the risk value between the proposed method and the traditional method increases dramatically as the uncertainty of soil properties becomes significant.

A geotechnical engineer could use the proposed method to perform slope failure analysis.

The failure consequence of a landslide can be rationally predicted using the proposed method.

The railway signal equipment failure diagnosis is a vital element to keep the railway system operating safely. One of the most difficulties in signal equipment failure diagnosis is the uncertainty of causality between the consequence and cause for the accident. The traditional method to solve this problem is based on Bayesian Network, which needs a rigid and independent assumption basis and prior probability knowledge but ignoring the semantic relationship in causality analysis. This paper aims to perform the uncertainty of causality in signal equipment failure diagnosis through a new way that emphasis on mining semantic relationships.

This study proposes a deterministic failure diagnosis (DFD) model based on the question answering system to implement railway signal equipment failure diagnosis. It includes the failure diagnosis module and deterministic diagnosis module. In the failure diagnosis module, this paper exploits the question answering system to recognise the cause of failure consequences. The question answering is composed of multi-layer neural networks, which extracts the position and part of speech features of text data from lower layers and acquires contextual features and interactive features of text data by Bi-LSTM and Match-LSTM, respectively, from high layers, subsequently generates the candidate failure cause set by proposed the enhanced boundary unit. In the second module, this study ranks the candidate failure cause set in the semantic matching mechanism (SMM), choosing the top 1st semantic matching degree as the deterministic failure causative factor.

Experiments on real data set railway maintenance signal equipment show that the proposed DFD model can implement the deterministic diagnosis of railway signal equipment failure. Comparing massive existing methods, the model achieves the state of art in the natural understanding semantic of railway signal equipment diagnosis domain.

It is the first time to use a question answering system executing signal equipment failure diagnoses, which makes failure diagnosis more intelligent than before. The EMU enables the DFD model to understand the natural semantic in long sequence contexture. Then, the SMM makes the DFD model acquire the certainty failure cause in the failure diagnosis of railway signal equipment.

The purpose of this paper is to develop a risk‐based integrity model for the optimal replacement of offshore process components, based on the likelihood and consequence of failure arising from time‐dependent degradation mechanisms.

Risk is a combination of the probability of failure and its likely consequences. Offshore process component degradation mechanisms are modeled using Bayesian prior‐posterior analysis. The failure consequences are developed in terms of the cost incurred as a result of failure, inspection and maintenance. By combining the cumulative posterior probability of failure and the equivalent cost of degradations, the operational life‐risk curve is produced. The optimal replacement strategy is obtained as the global minimum of the operational risk curve.

The offshore process component degradation mechanisms are random processes. The proposed risk‐based integrity model can be used to model these processes effectively to obtain an optimal replacement strategy. Bayesian analysis can be used to model the uncertainty in the degradation data. The Bayesian posterior estimation using an M‐H algorithm converged to satisfactory results using 10,000 simulations. The computed operational risk curve is observed to be a convex function of the service life. Furthermore, it is observed that the application of this model will reduce the risk of operation close to an ALARP level and consequently will promote the safety of operation.

The developed model is applicable to offshore process components which suffer time‐dependent stochastic degradation mechanisms. Furthermore, this model is developed based on an assumption that the component degradation processes are independent. In reality, the degradation processes may not be independent.

The developed methodology and models will assist asset integrity engineers/managers in estimating optimal replacement intervals for offshore process components. This can reduce operating costs and resources required for inspection and maintenance (IM) tasks.

The frequent replacement of offshore process components involves higher cost and risk. Similarly, the late replacement of components may result in failure and costly breakdown maintenance. The developed model estimates an optimal replacement strategy for offshore process components suffering stochastic degradation. Implementation of the developed model improves component integrity, increases safety, reduces potential shutdown and reduces operational cost.

Steam explosions are a major safety concern in many modern furnaces. The explosions are sometimes caused by water ingress into the furnace from leaks in its high-pressure (HP) cooling water system, coming into contact with molten matte. To address such safety issues related to steam explosions, risk based inspection (RBI) is suggested in this paper. RBI is presently one of the best-practice methodologies to provide an inspection schedule and ensure the mechanical integrity of pressure vessels. The application of RBIs on furnace HP cooling systems in this work is performed by incorporating the proportional hazards model (PHM) with the RBI approach; the PHM uses real-time condition data to allow dynamic decision-making on inspection and maintenance planning.

To accomplish this, a case study is presented that applies an HP cooling system data with moisture and cumulated feed rate as covariates or condition indicators to compute the probability of failure and the consequence of failure (CoF), which is modelled based on the boiling liquid-expanding vapour explosion (BLEVE) theory.

The benefit of this approach is that the risk assessment introduces real-time condition data in addition to time-based failure information to allow improved dynamic decision-making for inspection and maintenance planning of the HP cooling system. The work presented here comprises the application of the newly proposed methodology in the context of pressure vessels, considering the important challenge of possible explosion accidents due to BLEVE as the CoF calculations.

This paper however aims to optimise the inspection schedule on the HP cooling system, by incorporating PHM into the RBI methodology, as was recently proposed in the literature by Lelo et al. (2022). Moisture and cumulated feed rate are used as covariate. At the end, risk mitigation policy is suggested.

In this paper, the proposed methodology yields a dynamically calculated quantified risk, which emphasised the imperative for mitigating the risk, as well as presents a number of mitigation options, to quantifiably affect such mitigation.

Purpose: The emotions that accompany failure, in and of organizations, and their consequences have been researched in multiple domains of management, but comparative approaches have seldom been attempted. The failure of organizations to survive has been a common occurrence over centuries, particularly in the modern era of start-ups, innovation, and political, economic, and environmental turbulence. With the advent of the COVID-19 pandemic, failure at many levels of society, including the organizational and individual, has increased significantly and produced even more intense emotions. Study Design/Methodology/Approach: For this conceptual chapter, literature from many disciplines was consulted on failure in organizations, and the emotions it elicit, including studies on the process of failure as well as its outcomes. Findings: Failing and failure are likely to evoke negative emotions, with negative consequences for the actor. However, positive emotions can also occur, and a matrix of emotional valence and consequences presents an intriguing set of possibilities. The dimensions of emotions (valence, intensity, duration, and frequency) interact with a wide range of contributing factors (salience, personality, identity, emotional intelligence, emotional regulation, prior experience of failure, and context) in producing the emotions of failure and their consequences. Originality/Value: This chapter contributes to the literature by explicating the types of emotions that emanate during and after failure across many domains of management research, their dimensions and contributing factors, and the consequences for the individual actor. The model of the emotions of failure that is presented here assembles a wider variety of elements than prior research has offered. We indicate avenues for further research as we approach an era of even more demanding challenges.

Limited funding to maintain and preserve short‐line railroad (SLRR) bridge infrastructure requires that important priority decisions be made on an annual basis. The compartmentalized, dispersed, and diverse nature of many SLRR owners and operators is such that there is a need for a coordinated and centralized effort to evaluate the state‐wide system as a whole, to ensure the most effective overall resource allocation and also identify assets that either outperform predictions or consume disproportionate levels of resources for maintenance and operation, allowing for review of design and construction practices. The purpose of this paper is to examine the state of the art for railroad bridge population management and resource allocation decisions and to develop a state‐wide SLRR bridge prioritization methodology, to be used as a tool by a state agency to assist in allocating limited public funding for bridge maintenance, rehabilitation and replacement activities.

A literature review examining the state of the art of railroad bridge population management and resource allocation decisions was conducted, which provided the foundation for the development of a bridge prioritization algorithm. A state‐wide survey was conducted to develop a bridge database. A detailed evaluation of a statistically significant sample of bridges was conducted, to determine the structural and maintenance needs and preservation status of sub‐populations. The research team developed methodologies, applicable to the entire population, to develop a ranking of bridge preservation candidates.

A risk‐based prioritization algorithm is proposed to assign a relative risk score to each bridge in the population. The algorithm provides a management tool for making more effective maintenance and preservation decisions. Additionally, the bridge database allows managers to examine sub‐populations according to structural parameters to evaluate performance.

The revisable, modular framework of the prioritization algorithm provides a simple, effective and versatile tool for asset management and evaluation. The present proposal of this new prioritization methodology for SLRR bridges is a valuable tool for agencies faced with making rational decisions with limited information. Such a methodology does not currently exist in the literature and is of significant interest to short‐line owners/operators and state transportation agencies.

Asset intensive process industries are under immense pressure to achieve promised return on investments and production targets. This can be accomplished by ensuring the highest level of availability, reliability and utilization of the critical equipment in processing facilities. In order to achieve designed availability, asset characterization and maintainability play a vital role. The most appropriate and effective way to characterize the assets in a processing facility is based on risk and consequence of failure. The paper aims to discuss these issues.

In this research, a risk-based stochastic modeling approach using a Markov decision process is investigated to assess a processing unit's availability, which is referred as the risk-based availability Markov model (RBAMM). RBAMM will not only provide a realistic and effective way to identify critical assets in a plant but also a method to estimate availability for efficient planning purposes and resource optimization.

A unique risk matrix and methodology is proposed to determine the critical equipment with direct impact on the availability, reliability and safety of the process. A functional block diagram is then developed using critical equipment to perform efficient modeling. A Markov process is utilized to establish state diagrams and create steady-state equations to calculate the availability of the process. RBAMM is applied to natural gas absorption process to validate the proposed methodology. In the conclusion, other benefits and limitations of the proposed methodology are discussed.

A new risk-based methodology integrated with Markov model application of the methodology is demonstrated using a real-life application.